Add table-transformer to NormalizedConfigManager

optimum/utils/normalized_config.py

@@ -242,7 +242,6 @@ class NormalizedConfigManager:
         'segformer',
         'siglip',
         'squeezebert',
-        'table-transformer',
         ...
     """
 
@@ -303,6 +302,7 @@ class NormalizedConfigManager:
         "segformer": NormalizedSegformerConfig,
         "speech_to_text": SpeechToTextLikeNormalizedTextConfig,
         "splinter": NormalizedTextConfig,
+        "table-transformer": NormalizedTextConfig,
         "t5": T5LikeNormalizedTextConfig,
         "trocr": TrOCRLikeNormalizedTextConfig,
         "vision-encoder-decoder": NormalizedEncoderDecoderConfig,

tests/utils/test_normalized_config.py

@@ -0,0 +1,39 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import TableTransformerConfig
+
+from optimum.utils.normalized_config import NormalizedConfigManager, NormalizedTextConfig
+
+
+class NormalizedConfigManagerTest(unittest.TestCase):
+    def test_table_transformer_is_registered(self):
+        config_class = NormalizedConfigManager.get_normalized_config_class("table-transformer")
+        self.assertIs(config_class, NormalizedTextConfig)
+
+    def test_table_transformer_normalizes_expected_attributes(self):
+        # Same situation as DETR (table-transformer is DETR-based): the config
+        # stores d_model / encoder_attention_heads / encoder_layers, but its own
+        # attribute_map aliases these to hidden_size / num_attention_heads /
+        # num_hidden_layers, so plain NormalizedTextConfig needs no custom mapping.
+        config = TableTransformerConfig()
+        normalized_config_class = NormalizedConfigManager.get_normalized_config_class("table-transformer")
+        normalized_config = normalized_config_class(config)
+
+        self.assertEqual(normalized_config.hidden_size, config.d_model)
+        self.assertEqual(normalized_config.num_attention_heads, config.encoder_attention_heads)
+        self.assertEqual(normalized_config.num_layers, config.encoder_layers)